Persuasive discourse and language planning in Ireland

Colonial language discourse typically consists of evaluations concerning the respective merits of two or more languages, and the cultures they represent. This can serve as a warrant for imposing a ‘superior’ language. Although such discourse tends to be associated with the conquest of the New World and subsequent European expansion, there is evidence that in the case of Ireland – England’s first overseas colony – an adversarial relation between English and Irish languages existed even before the Elizabethan period. Referring to English legislation, chronicles and other documents, this paper examines the norms, arguments and rhetorical strategies that were used to exert the dominance of English language in Ireland during late-medieval and early-modern times. In the latter half of the paper, the focus will shift to attempts to create, especially from the seventeenth-century onwards, a ‘pro-Irish reversal’ that used similar arguments and rhetoric to reclaim this denigrated language. Our suggestion is that these pro- and anti-colonial language discourses anticipate those that were used later on in colonial and postcolonial environments

A Journal for the Astronomical Computing Community?

One of the Birds of a Feather (BoF) discussion sessions at ADASS XX considered whether a new journal is needed to serve the astronomical computing community. In this paper we discuss the nature and requirements of that community, outline the analysis that led us to propose this as a topic for a BoF, and review the discussion from the BoF session itself. We also present the results from a survey designed to assess the suitability of astronomical computing papers of different kinds for publication in a range of existing astronomical and scientific computing journals. The discussion in the BoF session was somewhat inconclusive, and it seems likely that this topic will be debated again at a future ADASS or in a similar forum.Comment: 4 pages, no figures; to appear in proceedings of ADASS X

Maximum Inner-Product Search using Tree Data-structures

The problem of {\em efficiently} finding the best match for a query in a given set with respect to the Euclidean distance or the cosine similarity has been extensively studied in literature. However, a closely related problem of efficiently finding the best match with respect to the inner product has never been explored in the general setting to the best of our knowledge. In this paper we consider this general problem and contrast it with the existing best-match algorithms. First, we propose a general branch-and-bound algorithm using a tree data structure. Subsequently, we present a dual-tree algorithm for the case where there are multiple queries. Finally we present a new data structure for increasing the efficiency of the dual-tree algorithm. These branch-and-bound algorithms involve novel bounds suited for the purpose of best-matching with inner products. We evaluate our proposed algorithms on a variety of data sets from various applications, and exhibit up to five orders of magnitude improvement in query time over the naive search technique.Comment: Under submission in KDD 201

Managing Research Data: Gravitational Waves

The project which led to this report was funded by JISC in 2010–2011 as part of its ‘Managing Research Data’ programme, to examine the way in which Big Science data is managed, and produce any recommendations which may be appropriate. Big science data is different: it comes in large volumes, and it is shared and exploited in ways which may differ from other disciplines. This project has explored these differences using as a case-study Gravitational Wave data generated by the LSC, and has produced recommendations intended to be useful variously to JISC, the funding council (STFC) and the LSC community. In Sect. 1 we define what we mean by ‘big science’, describe the overall data culture there, laying stress on how it necessarily or contingently differs from other disciplines. In Sect. 2 we discuss the benefits of a formal data-preservation strategy, and the cases for open data and for well-preserved data that follow from that. This leads to our recommendations that, in essence, funders should adopt rather light-touch prescriptions regarding data preservation planning: normal data management practice, in the areas under study, corresponds to notably good practice in most other areas, so that the only change we suggest is to make this planning more formal, which makes it more easily auditable, and more amenable to constructive criticism. In Sect. 3 we briefly discuss the LIGO data management plan, and pull together whatever information is available on the estimation of digital preservation costs. The report is informed, throughout, by the OAIS reference model for an open archive. Some of the report’s findings and conclusions were summarised in [1]. See the document history on page 37

On the Sample Complexity of Predictive Sparse Coding

The goal of predictive sparse coding is to learn a representation of examples as sparse linear combinations of elements from a dictionary, such that a learned hypothesis linear in the new representation performs well on a predictive task. Predictive sparse coding algorithms recently have demonstrated impressive performance on a variety of supervised tasks, but their generalization properties have not been studied. We establish the first generalization error bounds for predictive sparse coding, covering two settings: 1) the overcomplete setting, where the number of features k exceeds the original dimensionality d; and 2) the high or infinite-dimensional setting, where only dimension-free bounds are useful. Both learning bounds intimately depend on stability properties of the learned sparse encoder, as measured on the training sample. Consequently, we first present a fundamental stability result for the LASSO, a result characterizing the stability of the sparse codes with respect to perturbations to the dictionary. In the overcomplete setting, we present an estimation error bound that decays as \tilde{O}(sqrt(d k/m)) with respect to d and k. In the high or infinite-dimensional setting, we show a dimension-free bound that is \tilde{O}(sqrt(k^2 s / m)) with respect to k and s, where s is an upper bound on the number of non-zeros in the sparse code for any training data point.Comment: Sparse Coding Stability Theorem from version 1 has been relaxed considerably using a new notion of coding margin. Old Sparse Coding Stability Theorem still in new version, now as Theorem 2. Presentation of all proofs simplified/improved considerably. Paper reorganized. Empirical analysis showing new coding margin is non-trivial on real dataset

A solar water heater for remote communities

The RADG has been developing a solar water heater suitable for use in remote areas. The original inspiration for this project was to provide hot water for remote Aboriginal communities. It was felt that a regular and plentiful supply of hot water would encourage showering and laundering and hence improve personal hygiene. Electric, fuel burning and solar water heaters are currently used in some communities. Solar water heaters are attractive for remote areas because they stand alone i.e. they require no external fuel source. Wood has traditionally been used as a fuel by Aboriginal people, but in permanent communities the demand on this resource may have a large impact on the environment. Solar water heaters can help to reduce this demand